Elad Hoffer
Impact in
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- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Video Surveillance and Tracking Methods
- Image Retrieval and Classification Techniques
- Face recognition and analysis
- Multimodal Machine Learning Applications
- Artificial Intelligence top 2%
- Domain Adaptation and Few-Shot Learning
- Topic Modeling
Papers in
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- Advanced Neural Network Applications 4
- Advanced Image and Video Retrieval Techniques 1
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- Stochastic Gradient Optimization Techniques 2
- Domain Adaptation and Few-Shot Learning 2
- Neural Networks and Applications 1
- Co-authors
- Nir Ailon (1 shared paper)Daniel Soudry (5 shared papers)Itay Hubara (4 shared papers)Tal Ben‐Nun (1 shared paper)Niv Giladi (1 shared paper)Torsten Hoefler (1 shared paper)Tomer Michaeli (1 shared paper)Ron Banner (1 shared paper)
- Journals
- Neural Computation (1 paper)Lecture notes in computer science (2 papers)IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (2 papers)neural information processing systems (1 paper)
- Partner nations
- IsraelSwitzerland
In The Last Decade
Elad Hoffer
7 papers receiving 1.6k citations
Elad Hoffer's Hit Papers
Peers
Comparison fields: 5 of 88
- Computer Vision and Pattern Recognition 881
- Artificial Intelligence 602
- Signal Processing 110
- Media Technology 35
- Information Systems 74
Countries citing papers authored by Elad Hoffer
This map shows the geographic impact of Elad Hoffer's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Elad Hoffer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Elad Hoffer more than expected).
Fields of papers citing papers by Elad Hoffer
This network shows the impact of papers produced by Elad Hoffer. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Elad Hoffer. The network helps show where Elad Hoffer may publish in the future.
Co-authors
The 8 scholars most cited alongside Elad Hoffer, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Deep Metric Learning Using Triplet Network Hit paper breakdown → | 2015 | 1458 |
| 2 | 2020 | 113 | |
| 3 | 2020 | 73 | |
| 4 | Train longer, generalize better: closing the generalization gap in large batch training of neural networks | 2017 | 52 |
| 5 | 2021 | 9 | |
| 6 | Quantized Back-Propagation: Training Binarized Neural Networks with Quantized Gradients | 2018 | 4 |
| 7 | 2023 | 1 |
About Elad Hoffer
Elad Hoffer is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Electrical and Electronic Engineering, Nuclear Energy and Engineering and Waste Management and Disposal, having authored 7 papers that have together received 1.7k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (4 papers), Stochastic Gradient Optimization Techniques (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Neural Networks and Applications (1 paper), Advanced Memory and Neural Computing (1 paper), Low-power high-performance VLSI design (1 paper) and Advanced Image and Video Retrieval Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (881 citations), Artificial Intelligence (602 citations), Signal Processing (110 citations), Media Technology (35 citations) and Information Systems (74 citations). Elad Hoffer has collaborated with scholars based in Israel and Switzerland. Frequent co-authors include Nir Ailon, Daniel Soudry, Itay Hubara, Tal Ben‐Nun, Niv Giladi, Torsten Hoefler, Tomer Michaeli and Ron Banner. Their work appears in journals such as Neural Computation, Lecture notes in computer science, IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings and neural information processing systems.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.